A wide variety of different techniques are known for detecting edges in images. Such techniques generally produce acceptable results when applied to high-resolution images, such as photographs or other two-dimensional (2D) images produced by a digital camera. However, many important machine vision applications utilize three-dimensional (3D) images generated by depth imagers such as structured light (SL) cameras or time of flight (ToF) cameras. These depth images are often low-resolution images and typically include highly noisy and blurred edges.
Conventional edge detection techniques generally do not perform well when applied to depth images. For example, these conventional techniques may either miss important edges in a given depth image or locate multiple spurious edges along with the important edges. The resulting detected edges are of poor quality and therefore undermine the effectiveness of subsequent image processing operations such as feature extraction, pattern identification, gesture recognition, object recognition and tracking.